Automatic determination of baselines for battery testing

ABSTRACT

Baseline values for battery testing are automatically determined for individual batteries, battery cells, or networks of batteries. Impedance information is obtained from individual batteries and adjusted for operating conditions at a site of use (e.g., temperature, age, connection topology and user-entered information). Population-referenced baselines are automatically calculated from the group of individual-referenced baselines. All baselines can be continually updated and improved. SOC and SOH characteristics of batteries in the network can be automatically determined by comparison of measured impedance, and other, values to said baselines.

FIELD OF THE INVENTION

The present invention relates generally to testing electroniccomponents.

BACKGROUND

Equipment for measuring phase-sensitive impedance or admittance(collectively, vector parameters), or phase-insensitive resistance orconductance (collectively, scalar parameters) on batteries,particularly, but not limited to, lead-acid batteries, is widelyavailable. Technical expertise is often needed to interpret the raw dataoutputted by such instruments, and to understand the implications of thedata for battery maintenance and replacement.

It is presently not possible to accurately and consistently correlateimpedance parameters with a condition of a battery, such as state ofcharge (SOC), or retained capacity (RC), cold cranking amps (CCA), orwith post-mortem assessments of failure modes such as sulfation, dryout,or electrolyte decomposition (all broadly categorized as State of Health(SOH) characteristics), without using some sort of reference value as abaseline with which to compare parameter readings obtained. The accuracyof determinations of a SOC or SOH condition is dependent on the accuracyof the baseline used. However, there is at present no entirelysatisfactory method available for obtaining baseline values for measuredvector and scalar parameters of batteries.

This same fundamental need for a baseline reference applies whether theuser is attempting to determine a condition of an individual battery, ofa battery in a network, or of a network of batteries as a whole. Itapplies when the user is attempting to screen a population ofnon-networked batteries for purposes such as deciding whether to includethe individual batteries in a network. It also applies whether the useris utilizing a snapshot or a continuous approach to testing.

Some battery manufacturers, and some battery test equipmentmanufacturers, publish reference lists of typical conductance or otherscalar parameters for specific battery models in an effort to addressthis issue. The assumption is made that a reference scalar parametervalue obtained from a generic, new battery will provide a validbaseline. But, there are several problems with reference lists derivedunder pristine conditions from battery samples that are not specific tothe actual device during actual use in the field.

First, scalar parameter measurements tend to be instrument-specific, asthere is a wide variety of techniques and frequencies used by thedifferent manufacturers, and it is well known in the industry that theresults from different instruments, and even different instruments ofthe same make and model, are far from identical.

Second, although a typical scalar reference parameter value derived inthis way represents the mean for a large number of batteries, values forindividual batteries can deviate widely from the mean. In fact the rangeof values found among nominally identical batteries from a singlemanufacturer often exceeds the range in values expected for a singlehealthy battery over its lifespan. Furthermore, average parameter valuescan change substantially with even minor manufacturing changes.

Third, the temperature dependence of reference values is typically notconsidered. Vector and scalar measurements of electrochemical systemsvary markedly with temperature. This variation is typically not takeninto account with published reference values.

Fourth, reference values collected under ideal conditions may bearlittle similarity to the true reference values for individual batteriesin their operating environment. At a minimum, these reference values areuniversally collected on batteries at open circuit potential, while manyfield measurements are done on batteries being actively charged, whichchanges the vector and scalar measurement parameters of the battery.Furthermore, it is well known in the industry that most types ofbattery, and particularly lead acid batteries, undergo the final stagesof their formation processes after they enter use. The vector and scalarparameters of the battery will change with these final bedding in stagesand any accurate reference value must take account of these changes.

Fifth, published reference scalar parameter values typically refer to asingle frequency point, or to a single DC measurement value.Multi-frequency battery testing equipment is now emerging, with apotentially infinite number of frequencies at which both scalar andvector parameters can be measured. These parameters may vary markedlywith frequency. Thus a single scalar reference value offers minimal orno utility to these instruments.

Therefore, what is needed are techniques for automatically determiningbaselines for battery testing of batteries under actual operatingconditions and specific to the test instrument model in use, and thevector and scalar parameters, or any other measurement parameters, it isdesigned to measure.

SUMMARY

These needs are met by a method, system and computer program product forautomatically determining baseline impedance parameter values forbattery testing. The baseline values can be expressed as one or morevector or scalar quantities of Ohm units, or other units of vector andscalar measurement. In one embodiment, the baseline values are specificto the test instrument, to the battery or network of batteries, and totheir operating environment at a site of use. The baseline values can bedetermined for a network of batteries, for individual batteries withinthe network of batteries, or for cells within an individual battery.

In one embodiment, impedance information is obtained from individualbatteries or cells within the network of batteries by analyzing theresponse to a sinusoidal excitation signal at one or more frequencies,when the network is in use or “live”, in its topology of use, withelements providing charge and acting as potential loads fully connected.The impedance information collected under these conditions automaticallycompensates for the variations due to network topology or otheroperating conditions. From the impedance information,individual-referenced baselines can be calculated for each of thebatteries. The individual-referenced baselines are adjusted foroperating conditions at a site of use (e.g., temperature, battery age,or other user-entered information). Population-referenced baselines canthen be calculated from the set of individual-referenced baselines.

In some embodiments, the population-referenced baseline is updated byimpedance parameter values of the network of batteries measured atdifferent points in time, which process may occur relatively frequently,or widely separated in time, and which can result in the convergence ofbaselines on the most accurate possible value. In other embodiments, thepopulation-referenced baseline is an input to determining a condition ofbatteries or cells within the network, or of the network of batteries asa whole.

In some embodiments, other non-impedance information is used as part ofthe baseline, in addition to impedance data, including but not limitedto battery temperature, terminal temperature, a differential intemperature between battery terminals, electrolyte specific gravity,state of charge as determined by coulomb counting, or voltage.

Advantageously, and critically, battery test results are tailored tooperating conditions for a more accurate assessment of a condition of abattery. The battery results also take into account variations intesting equipment and battery manufacturing. The impedance informationused in determining a battery condition, by comparison, to a baseline,is the same type of impedance information, and is derived by the sametype of instrument or even the same instrument, as was used indetermining that baseline.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following drawings like reference numbers are used to refer tolike elements. Although the following figures depict various examples ofthe invention, the invention is not limited to the examples depicted inthe figures.

FIG. 1 is a block diagram illustrating a system to determine a baselineassociated with a network of batteries, according to an embodiment ofthe present invention.

FIG. 2 is a schematic diagram illustrating a testing device of thesystem in FIG. 1, according to an embodiment of the present invention.

FIG. 3 is a block diagram illustrating a testing module of the system inFIG. 1, according to an embodiment of the present invention.

FIG. 4 is a schematic diagram illustrating individual impedance andother information for a network of batteries and resulting baselines,according to an embodiment of the present invention.

FIG. 5 is a flow diagram illustrating a method for automaticallydetermining baselines associated with a network of batteries, accordingto an embodiment of the present invention.

FIG. 6 is a flow diagram illustrating a method for determining anindividual-referenced baseline, according to an embodiment of thepresent invention.

FIG. 7 is a flow diagram illustrating a method for determining apopulation-referenced baseline, according to an embodiment of thepresent invention.

FIG. 8 is a flow diagram illustrating a method for adjustingindividual-referenced baselines, both manually and automatically, in thelight of further collected impedance data and by a process ofconvergence, according to an embodiment of the present invention.

DETAILED DESCRIPTION

A system, method and computer program product for automaticallydetermining the baseline reference values associated with a network ofbatteries is disclosed.

As used herein, a “battery” is understood to refer to the individualtest element in a network or non-networked group of said elements,whether it be a single cell or a block of cells, or even the network ofelements in its entirety.

As used herein, “network” is understood to refer to an array ofelectrochemical energy storage devices, typically batteries or cells,the individual elements of which are joined together in any type ofseries or parallel topology, or any combination thereof, and which mayinclude electrical elements designed to provide charge to said energystorage devices, and also may include elements which will source powerfrom said energy storage devices by acting as electrical loads.

As used herein, “baseline” is understood to refer to a reference valueor values for an impedance, or other, parameter, representing theexpected value of that parameter in a battery at its maximum or idealSOH and SOC.

As used herein, an “individual-referenced baseline” is understood torefer to the baseline specific to any individual battery, which allowsthe user to measure and monitor the evolution of SOC and SOHcharacteristics of that battery over time by comparing it to measured orcalculated impedance, or other, parameter values collected from thatbattery at various time intervals, among other uses.

As used herein, a “population-referenced baseline” refers to thebaseline specific to a network of batteries connected together, andderived mathematically from the individual-referenced baselines of theindividual batteries of the network. It allows the user to assess theuniformity of SOC and SOH characteristics of the batteries within thenetwork, to detect population outliers, and to assess SOC and SOHcharacteristics of the network as a whole by comparing it to measured orcalculated impedance, or other, parameter values obtained from theindividual batteries of the network.

Furthermore, it is understood that when used alone herein, “baseline”refers to both individual-referenced and population-referencedbaselines, unless the context makes it clear that this is not the case.

As used herein, “convergence” refers to an iterative process of baselinerefinement by the incorporation of increasing amounts of data.Predictions are compared to actual measurements, and the outcome witheach datum input will be expected to move the calculated baseline closerto the true value, but in a manner where oscillation about the truevalue may be seen. The true value will be that which allows a conditionof a battery or a network, comprising a SOC or SOH characteristic, to bemost accurately estimated by algorithmic interpretation of subsequentimpedance, or other, measurements.

As used herein, “impedance”, or “impedance parameter”, are understood torefer to any measured vector or scalar parameter value or values, or anyrepresentation or construct mathematically derived from a measuredvector or scalar parameter value or values, of an electrical or anelectrochemical system, including but not limited to an energy-storagebattery.

As used herein, “snapshot” data collection refers to intermittent datacollection episodes using portable equipment, which episodes may bemonths or even years apart, and in which each data collection episode isinterpreted in isolation. Meanwhile, “continuous” data collection ormonitoring refers to data collected by equipment that is permanentlymounted or embedded in the battery or battery network underinvestigation, to collect data points at relatively frequent intervals,and evaluate changes in data relatively continuously.

As used herein, “SOC” and “SOH” characteristics are considered“conditions” of a battery or network of batteries. When applied to anindividual battery, they comprise a “condition” that affects orpotentially affects the ability of the battery to perform to userrequirements. When applied to a network of batteries, they comprise a“condition” that affects or potentially affects the ability of thenetwork of batteries to perform to user requirements, regardless of howmany individual batteries in the network are actually affected orpotentially affected by the “condition”. They include, but are notlimited to, battery characteristics such as retained capacity, coldcranking amps, retained percentage of charge and fuel gauging of batterynetworks and packs, cycle life remaining, life fraction, overchargingand undercharging, infant mortality defects, or failure modes such assulfation, dryout, or grid corrosion in lead acid batteries, failuremodes such as electrolyte decomposition and current collector pitting inlithium-ion batteries, and failure modes such as separator degradationor positive electrode capacity decay in nickel cadmium or nickel metalhydride batteries, as well as any other failure modes of any type ofbattery.

As used herein, the “calendar age” of a battery refers to the fractionof the battery's manufacturer-defined expected useful life, in years,that has been used up.

As used herein, the “life fraction” of a battery refers to the fractionof the battery's functional life that has been used up.

FIG. 1 is a block diagram illustrating a system 100 to determine abaseline associated with a network of batteries, according to anembodiment of the present invention. The system 100 comprises a networkof batteries 110, a testing device 120, a computing device 130, and aremote server 140. In one embodiment, the individual batteries in thenetwork of batteries 110 are sequentially connected to the testingdevice 120 during testing at a site 102 via electrical cord 103.Further, the testing device 120 is connected to the computing device 130during data transfer via channel 104 (e.g., a serial or parallel datacord, wired or wireless Ethernet channel, USB cord, FireWire cord, otherwireless connection, or the like), and which connection 104 may bepermanent. The computing device 130 and/or the testing device 120 mayalso communicate with a remote server 140, typically over a local areanetwork or wide area network.

The network of batteries 110 includes one or more individual electricalelements, such as batteries, 112 a-d. In one embodiment, the individualbatteries 112 a-d utilize lead acid chemistry, however, other batterychemistries can equally be used.

The individual batteries 112 a-d can be of any voltage or capacity usedfor residential, commercial, industrial, military or other use. Theywill typically be rechargeable secondary batteries, but primarybatteries are not excluded. They may be of any battery chemistry. Aconnection topology of the network of batteries 110 refers to a circuitconfiguration defining a flow of current between the main positive andnegative terminals of the network of batteries 110. For example, thenetwork of batteries 110 can be connected in series, in parallel, or anycombination two. The network may have parallel charging circuitry andequipment, or parallel electric loads and associated circuitry,complicating the network topology. In one application, the network ofbatteries 110 can be in active use to power a mobile system, such as anelectric-powered or hybrid-electric automobile, locomotive or crane. Inone application, the network of batteries 110 can be in reserve use asbackup power for a telecommunications system.

The testing device 120 can be, for example, a handheld device configuredwith hardware and firmware specific to battery testing. It may also be adevice configured to be permanently attached to each battery, or togroups of batteries within the network of batteries 110, and alsopermanently attached to the computing device 130, and such thatimpedance parameter, and other, data are acquired and processed on acontinuous or semi-continuous basis. In one embodiment, the testingdevice 120 generates and inputs a sinusoidal excitation signal orsignals of known frequency or frequencies, and known current amplitudeand phase, through each of the batteries in turn. The amplitude andphase shift of the voltage responses of the batteries to the excitationsignals at the various frequencies are measured, and used to derivevector impedance parameter values for the battery. In other embodiments,the excitation signal can be a square wave or a triangle wave, or avoltage or a current step, and the testing device 120 derives vector orscalar impedance values for the battery from the battery's response tothose excitation signals. In one implementation, the testing device 120is also able to measure one or more temperatures, voltage, specificgravity, and other associated characteristics of the batteries in thenetwork.

The computing device 130 can be a personal computer, a server blade, alaptop computer, a single-board computer, or any other type ofprocessor-controlled device. In one implementation, the testing device120 is used on site 102 for immediate, basic testing results while thecomputing device 130, having more processing power, a larger display anda more complete keyboard, can be used off site for further analysis andfor baseline modification and adjustment. Data can be uploaded to thecomputing device 130 in batch after collection from the sites, or inreal time through a wireless network connection, or other connection.Moreover, computing device 130 can be used to configure test settingsand download them, and also data such as historical or adjustedbaselines, to testing device 120. In one embodiment, the computingdevice 130 is part of a permanent, embedded continuous monitoring systemfor the network of batteries, and may also communicate with a remoteserver 140 which aggregates and may further process and analyze datafrom multiple battery networks, including baseline adjustment andmodification.

FIG. 2 is a schematic diagram illustrating an exemplary testing device120 of the system in FIG. 1, according to an embodiment of the presentinvention. The testing device 120 is a handheld device designed forsnapshot data collection and processing, and includes a display screen210 and a keypad 220. It is understood that testing device 120 is merelyan example which can be varied while remaining within the spirit of thepresent invention.

The testing device 120 can be enclosed in a casing made of suitablematerials, such as durable plastic with a rubber grip for ruggedenvironments. Additional components (not shown) within the casing aretypical for a mobile computing device. Namely, a motherboard with aprocessor (e.g. a RISC, FPGA or ASIC processor), memory (e.g., Flashmemory), an operating system (e.g., mobile version of Linux) andinput/output components, as described below in reference to FIG. 3. In aspecialized device, source code is preferably embedded in firmware.

In one implementation, a service person carries the testing device 120from one site to another to be used in troubleshooting or maintenance ofbattery backup power installations. In another implementation, thetesting device 120 is deployed in a laboratory environment in whichoperating conditions are simulated, as a research or development tool.

FIG. 3 is a block diagram illustrating a computing device 130 of thesystem 100 of FIG. 1, according to an embodiment of the presentinvention. The computing device 130 includes a memory 310, a hard drive320, a processor 330 and an input/output port 340. Alternativeconfigurations and additional components can serve the same functions asthe computing device 130 shown.

The memory 310 can be any type of memory device used to store sourcecode being executed. Examples include SRAM, DRAM, Flash, and the like.An operating system 312 such as Windows, Mac OS or Linux can execute inmemory along with various application software such as a testing module314.

The testing module 314, in one embodiment, provides deeper analysis fordata collected at various sites and a user interface. A user interfacepermits a user to, for example, generate graphs and manually manipulatethe data, as described in more detail below.

The testing module 314, in another embodiment, through specializedembedded code provides the ability to monitor and observe, as well as tomanage, data, baselines, and various conditions affecting the network ofbatteries to which it is permanently attached as part of an embedded,continuous monitoring system. It may also communicated with a remoteserver 140 which allows a user to, at a single location, view,aggregate, further process and analyze, data from multiple batterynetworks in real time, as well as to manage those networks based on theinformation received.

FIG. 4 is a schematic diagram illustrating one example of a userinterface 400 to the computing device 130. The computing device 130 canreceive information from the testing device 120 and display it on theuser interface 400. Individual batteries are plotted in graphic form inthe different graphs along an x-axis, while several measured andcalculated impedance, and other, values are plotted along a y-axis.Graph 402 shows the measured impedance value for each battery (in thiscase, the vector real impedance response at a specific frequency), withthe automatically-calculated population-referenced baseline 408 shown asthe green reference line 405. Warning and fail alarm lines, whichrepresent threshold deviations from the population-referenced baselineand which can be correlated to a battery condition, are shown 406, andtheir values as percentages 407 of the population-referenced baselinevalue 408. The reference line 405 may be moved up and down by theskilled user, as part of the process of convergence, adjusting thepopulation-referenced baseline 408, and the alarm lines 406 will followautomatically. The alarm percentage values 407 may also be adjustedindividually.

Graph 403 shows the same measured impedance of each battery in thenetwork, but in this case presented as a percentage change from itsindividual-referenced baseline value. Alarm lines are again shown, andagain they represent threshold deviations, but from theindividual-referenced baseline, and Again they can be correlated to abattery condition. No reference line is necessary, as theindividual-referenced baseline is by definition 100%. The presentationallows the user to easily visualize changes from baseline in individualbatteries.

Graph 404 shows the measured voltage, a non-impedance parameter carryinginformation about SOC and SOH characteristics, of each battery. Again,reference 409 and alarm 410 values are shown and graphed as lines 411and 412 respectively. The reference value 409 can be considered anautomatically-calculated population-referenced baseline, and can againbe adjusted by the skilled user as part of the process of convergence.Again, the alarm lines 412 represent threshold deviations from thepopulation-referenced baseline and can be correlated to a batterycondition.

FIG. 5 is a flow diagram illustrating a method for determining baselinesassociated with a network of batteries, according to an embodiment ofthe present invention. The method can be implemented in the system 100of FIG. 1.

A network of batteries is deployed 510 on a site for use. The group canbe deployed together, or individual batteries can be switched out asneeded. The batteries can be, for example, active in powering anelectrical system, or can be used to backup a grid-powered electricalsystem in case of a power failure.

As a procedure for troubleshooting or regular preventative maintenance,the deployed batteries are tested to ensure performance. To do so,individual impedance, based on the measured response of each battery toa known excitation signal information is obtained 520 from batterieswithin a network.

An individual-referenced baseline is calculated for each battery 530,based on the measured impedance values.

The individual-referenced baselines are adjusted 540 for factors suchas, but not limited to, on site operating conditions includingtemperature, calendar age, connection topology and user-enteredinformation. These adjustments are described in more detail withreference to FIG. 6. Adjustment proceeds by a combination of user inputsand automated processing.

A population-referenced baseline for the network as a whole iscalculated 550 from the group of individual-referenced baselines, asdescribed more fully below in reference to FIG. 7.

In one embodiment, a condition, comprising a SOC or SOH characteristic,may be determined 560 for individual batteries or cells, or for thenetwork of batteries as a whole, by algorithmically comparing theimpedance values measured for each battery to the population-referencedbaseline. In this case, the impedance information compared to thepopulation-referenced baseline will comprise the individual-referencedbaselines of each battery. More specifically, variations in theseimpedance values can indicate the presence and magnitude of a condition.Degradation or failures can occur due to, for example, sulfation,dryout, grid corrosion, or manufacturer defects. Also, disparities inbattery SOH can and do affect the overall health of a network, and itsability to deliver adequate power when required, by overburdeninghealthy batteries.

Note that the method 500 is a snapshot for one iteration of the processof determining baselines. In one embodiment, embedded continuousmonitoring supplies repeated snapshots for additional iterations. Thebaselines are updated and made increasingly accurate in a process ofconvergence, as described more fully below in reference to FIG. 8.

FIG. 6 is a flow diagram illustrating an example method 540 fordetermining an individual-referenced baseline, according to anembodiment of the present invention. In the embodiment shown, theindividual-referenced baseline is adjusted 610 for battery temperature.Alongside the impedance measurement, temperature of the battery ismeasured, in any one of a number of ways, as the impedance parameterswill vary markedly, but predictably, with temperature.

Individual-referenced or population-referenced baselines can also beadjusted 620 to account for a battery calendar age. Impedance tends tochange, in a predictable manner, with battery calendar age.Consequently, a baseline can be adjusted to an age of a battery toultimately enable more accurate determinations of conditions such as SOCand SOH characteristics of the battery or of a network. Of particularinterest, this adjustment is necessary to enable the determination of abattery life fraction, a condition of significant interest in batterymonitoring.

Furthermore, some embodiments adjust 630 individual-referenced baselinesfor a position of the individual battery within a connection topology.Impedance information can be affected by parallel versus seriesconnections, the number of batteries in a series network, the positionof a battery in a network, the configuration of battery interconnects,the position in the string relative to attached charging apparatus orelectrical loads, and the like. Any battery that is part of a networkwhich includes parallel connection of parts of the network will beexpected to have an impedance lower than would be the case without thoseparallel elements, for example. A battery which is the terminal elementin a series-connected network will be expected to have lower impedancethan other batteries within the network, as another example. A batterywhich, by dint of its position in the network topology has batteryconnection hardware applied to its terminals in different configurationfrom that typical for the batteries in the network, and particularly abattery with multiple positive and negative terminals, may show adifferent apparent measured impedance due to the effects of shunting bythe connection hardware, as a further example.

In one embodiment, individual-referenced baselines are also adjusted 640for user-entered information. For example, a user can provide historicaldata for measured impedance values that he wishes to use asindividual-referenced or population-referenced baselines. In anotherexample, a user can override automatically calculated baselines in favorof self-calculated or manufacturer-provided values. In a furtherexample, a user can indicate that he does not want theindividual-referenced baseline for a particular battery or batteries tobe used in calculating the population-referenced baseline, oralternatively that a particular individual-referenced baseline should beweighted in some way, such as over- or under-weighted, in calculatingthe population-referenced baseline.

Those skilled in the art will recognize that adjustments for certaincharacteristics of the operating environment, and certain variations intest instruments used, will be inherently accounted for during testingon-site without the need for an explicit adjustment. For example,differences between impedance readings obtained with test equipment fromdifferent manufacturers, or of different models from the samemanufacturer, or even of individual representatives of a single model oftesting device, are avoided by automatically generating a baseline usingthe same equipment that will be used in testing the batteries in future.Likewise, differences in network topology will be dealt with at theindividual-referenced baseline level, although not at thepopulation-referenced baseline level, by on-site baseline determination.Further, the impedance information used in determining a batterycondition, by comparison, to a baseline, is the same type of impedanceinformation as was used in determining that baseline.

FIG. 7 is a flow diagram illustrating an example of a method 550 fordetermining a population-referenced baseline, according to an embodimentof the present invention. More specifically, the most extremeindividual-referenced baseline, in the direction generally indicative ofan ideal SOH for the batteries in the network, is algorithmicallyidentified 710 for automated comparison 720 against an average value fora particular defined subgroup of the network of batteries, to determinewhether it is an outlier or a legitimate member of the distribution ofbaselines within the network.

If the most extreme individual-referenced baseline is ruled an outliervalue 730, it is disregarded in favor of the next-most extreme value735, which is in turn algorithmically tested to determine whether it isan outlier, and the process is iteratively repeated until a non-outlierbaseline is identified. The determination of outliers is implementationspecific. One implementation ignores outliers more than 5% below theaverage of the defined subgroup.

Once the most extreme individual-referenced baseline that is not anoutlier is identified, it is set 740 as the population-referencedbaseline.

It should be noted that this is just one of many possible approaches tothe automated calculation of a population-referenced baseline 550. Abaseline will not necessarily be a most extreme value, it may be amedian or average value, for example in the case of screening batteriesfor latent defects.

It is understood that the determination of conditions such as SOC andSOH characteristics of the batteries in the network 560 is a primary andimmediate goal of the user. However, it is equally recognized that theaccuracy of the qualification and quantification of thesecharacteristics is dependent on the quality of the baseline or baselinesused. Further, the more information that is available about individualbatteries, the more accurate can the baselines for those batteries beexpected to be.

FIG. 8 is a flow diagram illustrating the incorporation of further datainto the baselines for the network to make them, by a process ofconvergence, increasingly accurate. Impedance and non-impedance data,such as specific gravity (in flooded lead acid batteries, for example)or discharge capacity measurements, of individual batteries in thenetwork can be obtained 810 and incorporated to automatically adjustbaselines 820. Impedance data can also be used to automatically adjustbaselines 830. Particularly, but not exclusively, in the case ofcontinuous monitoring, the adjustments can be fine-tuned by feedback andinteraction between impedance measurement data and non-impedance data840. Predictions are compared to actual data in an iterative process,and resulting in increasing convergence of baselines on their truevalue. One example would be the automated algorithmic comparison ofchanges in impedance data over time to the calendar age-adjustmentutilized to compensate a baseline for battery calendar age, withsubsequent adjustment to that compensation. Another example would be theautomated algorithmic comparison of battery impedance data collected atdifferent, known, states of charge (as determined by non-impedancemethods such as coulomb counting), and where a variation in state ofcharge is known to be a surrogate for a battery condition, to determinea more accurate baseline related to that condition. Yet another examplewould be the automated algorithmic comparison of battery impedance datacollected with the battery at different, known, specific gravities(possibly determined by use of a conductance probe which is an accessoryto the testing device 120), and where a variation in specific gravity isknown to be a surrogate for a battery condition, to determine a moreaccurate baseline related to that condition. Further, baselines can beuser-critiqued and manually adjusted 850 in specialized software such asthat shown in FIG. 4.

In one embodiment of the invention, the battery network is in use topower an electric or hybrid-electric vehicle, such as an automobile, alocomotive, or an industrial crane. Impedance and non-impedance data isacquired from the network of batteries on a continuous orsemi-continuous basis. Baselines will be generated and updated by theconvergence process automatically, and SOC and SOH conditions determinedby comparison of further collected impedance and non-impedance data tothe baselines. In this case the testing device may be physicallyembedded as part of a modular battery network, or battery pack, and maycommunicate with a computing device in the vehicle to give desired SOCand SOH outputs such as a “fuel gauge” of, for example, range remaining.There may be further communication with a remote server, by wired orwireless means, which may act as a data aggregator from multiplevehicles, provide the ability to further modify baselines, and provideremote monitoring of the battery network SOH and SOC characteristics, aswell as having the ability to act on certain cues, such as automaticallynotifying the user of a developing catastrophic failure condition.

In one embodiment, most suited to continuous embedded monitoring of anetwork of batteries, and particularly but not exclusively where thebattery network is in use to power an electric or hybrid-electricvehicle, such as an automobile, a locomotive, or an industrial crane,the information used to develop baselines comprises a representation ofa dynamic response of a battery unit to a known perturbation, where theresponse is related to a condition of the battery, and where theperturbation is produced by the usage profile or environment to whichthe battery network is subject, rather than being induced by themeasuring instrument. For example, the dynamic response can consist of achange in an impedance parameter, or of battery voltage, in response toa quantified movement of charge into or out of the battery. As a furtherexample, the dynamic response can consist of a change in an impedanceparameter in response to a defined change in the state of charge of thebattery, or to a quantified change in temperature of the battery. Again,baselines will be generated and updated automatically by the convergenceprocess, and SOC and SOH conditions will be determined by comparison offurther collected impedance and non-impedance data to the baselines, butin this case the further collected impedance and non-impedance data willcomprise a representation of the dynamic response of a battery unit to alike known perturbation as was used in generating the baselines. Fulladvantage is taken of both the embedded monitoring capability, and ofthe presence of secondary perturbations to the battery network, and thebatteries comprising the network, which arise by nature of the purposeto which the network is put, and the response of the batteries to whichperturbations yield information about SOC and SOH conditions of thebatteries and the network which cannot be obtained using a snapshotmonitoring technique.

In a variation, the network baselines are generated with the aim ofrepresenting a reference value or values for an impedance parameter in abattery at a defined non-ideal SOH or SOC. The data is processed in thesame way as previously described to extract information on a battery ornetwork condition. When further testing of the batteries is performed,it is done so with the network of batteries in a like, non-ideal SOH orSOC as to when the baselines were generated. The intention is to amplifythe expression of a battery or network condition, and render it moreeasily detected and measured. For example, if impedance baselines aredetermined after discharging a network of lead-acid batteries to amarked degree, and further testing of the batteries is performed withthe network in a like discharged condition, then the degree ofdivergence from baseline of individual batteries in a low general SOHwill be markedly amplified from that seen if the same process wasperformed with the network of batteries fully charged. Again, thisvariation is particularly relevant in the case of continuous embeddedmonitoring of a network of batteries, and more particularly where thebattery network is in use to power an electric or hybrid-electricvehicle, such as an automobile, a locomotive, or an industrial crane.However, the technique may be utilized fully with snapshot datacollection, as well.

In another variation, individual-referenced baselines are used tomonitor the progress of battery conditioning and reconditioningtechniques. For example, current pulses of varying amplitude, durationand frequency may be applied to the terminals of a lead acid battery, orto the main positive and negative terminals of a network of suchbatteries, for the purpose of reversing sulfation or some otherdegradation process, or as a general conditioning strategy. Impedancedata (for example, from a Fourier transformation performed on thevoltage response of the battery to a current pulse) and non-impedancedata (for example, the terminal temperatures, as determined by a varietyof methodologies, and the temperature differential between the positiveand negative terminals) are used to automatically determine anindividual-referenced baseline for each battery. Then the progress ofthe conditioning or reconditioning process may be monitored by comparingimpedance and other data, further collected periodically during, andafter, the conditioning process, to the baseline. Those skilled in theart will recognize that this progress monitoring can be used toautomatically control or guide the conditioning or reconditioningprocess, increasing its efficacy and efficiency, as well as its ease ofapplication. Advantageously, the baseline used here will be specific andtailored to the technique in use, the instrument used, and to thebattery or batteries undergoing the process. Of further advantage, theconditioning process itself may be used to generate the data, andspecifically the impedance data, used initially in determining thebaseline, and then in monitoring or controlling the process. Note thatin this case, although the baseline is possibly a temporary one andrepresentative of the battery at a potentially degraded SOC or SOH, itis generated by the same methods, techniques and logic as are baselinesrepresenting an ideal SOC or SOH, and will have equal utility in thisspecific situation.

In summary, following initial calculation of individual-referencedbaselines for the batteries in a network, a population-referencedbaseline for the network is calculated, and initial determination of acondition or conditions, comprised of SOC and SOH characteristics, ofthe batteries of the network, and of the network itself, is performed bycomparison of individual-referenced baselines to thepopulation-referenced baseline. Further data is used to refineindividual-referenced and/or population-referenced baselines over timeby a process of convergence FIG. 8, and this enables the increasinglyaccurate determination of conditions, comprising SOC and SOHcharacteristics, of batteries of the network and of the network itself.

What has been described and illustrated herein is a preferred embodimentalong with some of its variations. The terms, descriptions and figuresused herein are set forth by way of illustration only and are not meantas limitations. Those skilled in the art will recognize that manyvariations are possible within the spirit and scope of the invention inwhich all terms are meant in their broadest, reasonable sense unlessotherwise indicated. Any headings utilized within the description arefor convenience only and have no legal or limiting effect.

While the invention has been described by way of example and in terms ofthe specific embodiments, it is to be understood that the invention isnot limited to the disclosed embodiments. To the contrary, it isintended to cover various modifications and similar arrangements aswould be apparent to those skilled in the art. Therefore, the scope ofthe appended claims should be accorded the broadest interpretation so asto encompass all such modifications and similar arrangements.

The invention claimed is:
 1. A method for automatically generatingbaselines for impedance-based battery testing, the method comprising:obtaining individual impedance information for each battery unit in anetwork of batteries by measuring the response of the battery to adefined electrical excitation signal; calculating anindividual-referenced baseline for each battery unit from the individualimpedance information; adjusting the individual-referenced baselines toaccount for a temperature associated with each battery unit; adjustingthe individual-referenced baselines to account for a calendar ageassociated with each battery unit, using a microprocessor; andcalculating a first population-referenced baseline from theindividual-referenced-baselines as adjusted, for a first point in time.2. The method of claim 1, further comprising: identifying a deviationfrom the population-referenced baseline that exceeds a pre-determinedthreshold and is correlated to a condition.
 3. The method of claim 1,further comprising: determining a condition for a battery in the networkof batteries by comparing the individual-referenced baseline of thebattery to the population-referenced baseline.
 4. The method of claim 3,further comprising: determining a distribution of the condition withinthe network of batteries by comparing the individual-referencedbaselines of the batteries in the network to the population-referencedbaseline.
 5. The method of claim 4, further comprising: determining acondition for the network of batteries as a whole by assessment of thedistribution of the condition within the network of batteries.
 6. Themethod of claim 1, further comprising: calculating a secondpopulation-referenced baseline in the same manner as the first, from asecond round of gathering individual impedance information at a secondpoint in time occurring subsequent to the first point in time; andupdating the first population-referenced baseline to include data fromthe second population-referenced baseline.
 7. The method of claim 1,further comprising: receiving a user adjustment to either one of theindividual-referenced baselines, or to the population-referencedbaseline.
 8. The method of claim 7, wherein receiving the useradjustment comprises receiving a reference baseline provided by amanufacturer.
 9. The method of claim 1, further comprising: receivingnon-impedance information, and performing an automated adjustment toeither one of the individual-referenced baselines, or to thepopulation-referenced baseline.
 10. The method of claim 9, wherein thenon-impedance information comprises one or more of a battery voltage, adifferential in battery voltage due to a quantified movement of chargeinto or out of a battery, a battery temperature, a battery terminaltemperature, a differential in temperature between battery terminalsrelated to a movement of charge, whether quantified or non-quantified,into or out of a battery, a battery electrolyte specific gravity, abattery or battery network life fraction, a battery amp hour capacity,or a battery or network state of charge as determined by coulombcounting.
 11. The method of claim 1, further comprising: determining acondition for a battery in the network of batteries by comparingimpedance information subsequently gathered from the battery to thepopulation-referenced baseline.
 12. The method of claim 11, furthercomprising: determining a distribution of the condition within thenetwork of batteries by comparing impedance information subsequentlygathered from the batteries in the network to the population-referencedbaseline.
 13. The method of claim 12, further comprising: determining acondition for the network of batteries as a whole by assessment of thedistribution of the condition within the network of batteries.
 14. Themethod of claim 1, further comprising: determining a condition for anindividual battery in the network of batteries by comparing impedanceinformation subsequently gathered from the battery to theindividual-referenced baseline for the battery.
 15. The method of claim14, further comprising: determining the evolution over time of acondition for an individual battery in the network of batteries bycomparing impedance information subsequently gathered from the batteryon separate occasions over time to the individual-referenced baselinefor the battery.
 16. The method of claim 1, wherein the impedanceinformation comprises a phase-sensitive vector parameter defined by amagnitude and a phase angle.
 17. The method of claim 1, wherein theimpedance information comprises a phase-insensitive scalar parameterdefined by a single numerical value and unit.
 18. The method of claim 1,wherein the impedance information comprises a representation orconstruct mathematically derived from a measured vector or scalarparameter value or values.
 19. The method of claim 1, wherein theimpedance information is collected while the network of batteries is ata known, nominally uniform, state of charge that is less than a fullstate of charge.
 20. The method of claim 1, wherein the network ofbatteries is in active use powering a hybrid-electric or electricautomobile, locomotive, or industrial crane.
 21. The method of claim 1,wherein the information collected comprises a combination of impedanceinformation and non-impedance information.
 22. A method forautomatically generating baselines for impedance-based battery testing,the method comprising: obtaining individual impedance information foreach battery unit in a network of batteries by measuring the response ofthe battery to a defined electrical excitation signal, wherein theimpedance information comprises a representation of a dynamic responseof an impedance parameter of one of the battery units to a knownperturbation, the dynamic response being related to a condition of thebattery unit, the perturbation being produced by the usage to which thebattery network is subject; calculating an individual-referencedbaseline for each battery unit from the individual impedanceinformation; adjusting the individual-referenced baselines to accountfor a temperature associated with each battery unit; adjusting theindividual-referenced baselines to account for a calendar age associatedwith each battery unit, using a microprocessor; and calculating a firstpopulation-referenced baseline from the individual-referenced-baselinesas adjusted, for a first point in time.
 23. The method of claim 22,wherein the perturbation consists of a quantified movement of chargeinto or out of the battery.
 24. The method of claim 23, wherein thequantified movement of charge takes place at a known beginning or endingstate of charge of the battery.
 25. The method of claim 22, wherein theperturbation consists of a quantified change in temperature of thebattery.
 26. The method of claim 22, wherein the perturbation consistsof a quantified change in a temperature differential between a positiveand negative terminal pair of the battery.
 27. The method of claim 22,further comprising: identifying a deviation from thepopulation-referenced baseline that exceeds a pre-determined thresholdand is correlated to a condition.
 28. The method of claim 22, furthercomprising: determining a condition for a battery in the network ofbatteries by comparing the individual-referenced baseline of the batteryto the population-referenced baseline.
 29. The method of claim 28,further comprising: determining a distribution of the condition withinthe network of batteries by comparing the individual-referencedbaselines of the batteries in the network to the population-referencedbaseline.
 30. The method of claim 29, further comprising: determining acondition for the network of batteries as a whole by assessment of thedistribution of the condition within the network of batteries.
 31. Themethod of claim 22, further comprising: calculating a secondpopulation-referenced baseline in the same manner as the first, from asecond round of gathering individual impedance information at a secondpoint in time occurring subsequent to the first point in time; andupdating the first population-referenced baseline to include data fromthe second population-referenced baseline.
 32. The method of claim 22,further comprising: receiving a user adjustment to either one of theindividual-referenced baselines, or to the population-referencedbaseline.
 33. The method of claim 32, wherein receiving the useradjustment comprises receiving a reference baseline provided by amanufacturer.
 34. The method of claim 22, further comprising: receivingnon-impedance information, and performing an automated adjustment toeither one of the individual-referenced baselines, or to thepopulation-referenced baseline.
 35. The method of claim 34, wherein thenon-impedance information comprises one or more of a battery voltage, adifferential in battery voltage due to a quantified movement of chargeinto or out of a battery, a battery temperature, a battery terminaltemperature, a differential in temperature between battery terminalsrelated to a movement of charge, whether quantified or non-quantified,into or out of a battery, a battery electrolyte specific gravity, abattery or battery network life fraction, a battery amp hour capacity,or a battery or network state of charge as determined by coulombcounting.
 36. The method of claim 22, further comprising: determining acondition for a battery in the network of batteries by comparingimpedance information subsequently gathered from the battery to thepopulation-referenced baseline.
 37. The method of 36, furthercomprising: determining a distribution of the condition within thenetwork of batteries by comparing impedance information subsequentlygathered from the batteries in the network to the population-referencedbaseline.
 38. The method of 37, further comprising: determining acondition for the network of batteries as a whole by assessment of thedistribution of the condition within the network of batteries.
 39. Themethod of claim 22, further comprising: determining a condition for anindividual battery in the network of batteries by comparing impedanceinformation subsequently gathered from the battery to theindividual-referenced baseline for the battery.
 40. The method of claim39, further comprising: determining the evolution over time of acondition for an individual battery in the network of batteries bycomparing impedance information subsequently gathered from the batteryon separate occasions over time to the individual-referenced baselinefor the battery.
 41. The method of claim 22, wherein the impedanceinformation comprises a phase-sensitive vector parameter defined by amagnitude and a phase angle.
 42. The method of claim 22, wherein theimpedance information comprises a phase-insensitive scalar parameterdefined by a single numerical value and unit.
 43. The method of claim22, wherein the impedance information comprises a representation orconstruct mathematically derived from a measured vector or scalarparameter value or values.
 44. The method of claim 22, wherein theimpedance information is collected while the network of batteries is ata known, nominally uniform, state of charge that is less than a fullstate of charge.
 45. The method of claim 22, wherein the network ofbatteries is in active use powering a hybrid-electric or electricautomobile, locomotive, or industrial crane.
 46. The method of claim 22,wherein the known perturbation is a time dependent voltage.
 47. Acomputer program product comprising program code stored on anon-transitory computer-readable medium for automatically generatingbaselines for impedance-based battery testing, the computer productcomprising program code for: obtaining individual impedance informationfor each battery unit in a network or other population of batteries byutilizing the response of the battery to a defined electrical excitationsignal; calculating an individual-referenced baseline for each batteryunit from the individual impedance information; adjusting theindividual-referenced baselines to account for a temperature associatedwith each battery unit; adjusting the individual-referenced baselines toaccount for a calendar age or life fraction associated with each batteryunit; and calculating a first population-referenced baseline from theindividual-referenced-baselines as adjusted, for a first point in time.48. The computer program product of claim 47, further comprising theprogram code for automatically: calculating a secondpopulation-referenced baseline in the same manner as the first, from asecond round of gathering individual impedance information at a secondpoint in time occurring subsequent to the first point in time; andupdating the first population-referenced baseline to include data fromthe second population-referenced baseline.
 49. The computer programproduct of claim 47, further comprising the computer code for: receivinga user adjustment to either one of the individual-referenced baselines,or to the population-referenced baseline.
 50. The computer programproduct of claim 49, further comprising the computer code for: receivingnon-impedance information, and performing an automated adjustment toeither one of the individual-referenced baselines, or to thepopulation-referenced baseline.
 51. The computer program product ofclaim 50, wherein the non-impedance information comprises one or more ofbattery voltage, a differential in battery voltage due to a quantifiedmovement of charge into or out of the battery, battery temperature,battery terminal temperature, a differential in temperature betweenbattery terminals related to a movement of charge, whether quantified ornon-quantified, into or out of the battery, electrolyte specificgravity, battery amp hour capacity or battery state of charge asdetermined by coulomb counting.
 52. A system for automaticallygenerating baselines for impedance-based battery testing, systemcomprising: means for obtaining individual impedance information foreach battery unit in a network or other population of batteries byutilizing the response of the battery to a defined electrical excitationsignal; means for calculating an individual-referenced baseline for eachbattery unit from the individual impedance information; means foradjusting the individual-referenced baselines to account for atemperature associated with each battery unit; means for adjusting theindividual-referenced baselines to account for a calendar age or lifefraction associated with each battery unit; and means for calculating afirst population-referenced baseline from theindividual-referenced-baselines as adjusted, for a first point in time.53. The system of claim 52, further comprising: means for calculating asecond population-referenced baseline in the same manner as the first,from a second round of gathering individual impedance information at asecond point in time occurring subsequent to the first point in time;and means for updating the first population-referenced baseline toinclude data from the second population-referenced baseline.
 54. Thesystem of claim 52, further comprising: means for receiving a useradjustment to either one of the individual-referenced baselines, or tothe population-referenced baseline.
 55. The system of claim 54, furthercomprising: means for receiving non-impedance information, and means forperforming an automated adjustment to either one of theindividual-referenced baselines, or to the population-referencedbaseline.
 56. The system of claim 55, wherein the non-impedanceinformation comprises one or more of battery voltage, a differential inbattery voltage due to a quantified movement of charge into or out ofthe battery, battery temperature, battery terminal temperature, adifferential in temperature between battery terminals related to amovement of charge, whether quantified or non-quantified, into or out ofthe battery, electrolyte specific gravity, battery amp hour capacity orbattery state of charge as determined by coulomb counting.